Abstract 5176: Tumor dynamic model-based decision support for phase Ib immunotherapy combination studies
Authors: René Bruno, Mathilde Marchand, Kenta Yoshida, Phyllis Chan, Haocheng Li, Wei Zou, Francois Mercier, Pascal Chanu, Benjamin Wu, Anthony Lee, Chunze Li, Jin Y. Jin
Journal: Cancer Research (2022)

Abstract Background: Tumor growth inhibition (TGI)-overall survival (OS) models predict OS distributions and study outcomes (OS hazard ratio (HR)) in a number of cancers and settings (Bruno, Clin Cancer Res 2020;26:1787-95). Model-derived TGI metrics such as tumor growth rate (KG) have been considered as a promising early endpoint compared to classical response criteria (Gong J Clin Oncol 2020; doi: 10.1200/JCO.2020.38.15_suppl.9541). This work is assessing the validity of tumor growth rate estimates to inform GO/noGO decisions for Phase Ib studies. Methods: We resampled baseline characteristics and longitudinal sum of longest diameters (SLD, RECIST 1.1) from a positive Phase III study (IMpower150, Socinski, N Engl J Med 2018;378:2288-301) to mimic a Phase Ib study of 15, 30, or 40 patients per arm (atezolizumab +bevacizumab+carboplatin+paclitaxel (A+BCP) vs. BCP) with 10 months recruitment and 6 or 3 months follow-up (FU) after the last patient recruited. Effect sizes across A+BCP vs. BCP or BCP vs. BCP were calculated as geometric mean ratio (GMR) for KG, difference (d) in objective response rate (ORR), and HR for progression-free survival (PFS) and simulated OS. For this, individual KG were estimated using a bi-exponential model (Claret, Clin Cancer Res 2018;24:3292-8); ORR and PFS were estimated per RECIST 1.1, OS was simulated using a previously published TGI-OS model (Chan CPT:PSP 2021; doi.org/10.1002/psp4.12686) with 400 patients per arm bootstrapped from the 15, 30 or 40 patients of the resampled subsamples. Correct and incorrect Go decisions were based on the probability (prob) to achieve target effect sizes in A+BCP vs. BCP and BCP vs. BCP, respectively, across 500 replicated subsamples. Results: To not exceed an incorrect Go decision of 20% and for 40 patients/6 months FU, correct Go based on prob(KG GMR&amp;lt;0.90), prob(dORR&amp;gt;0.10), prob(OS HR&amp;lt;0.85) and prob(PFS HR&amp;lt;0.70) were 83%, 69%, 67% and 58%, respectively. For 40 patients/3 months FU, 30 patients/6 or 3 months FU, the ranking did not change and prob(KG GMR&amp;lt;0.90) correct Go decision rate remained around 80%. Conclusions: This evaluation suggests that model-based estimates of on-treatment tumor growth rate could be used as an exploratory endpoint to inform early clinical decisions. Expansion of this work is planned in other settings e.g. single-arm studies commonly used in early oncology clinical development. Citation Format: René Bruno, Mathilde Marchand, Kenta Yoshida, Phyllis Chan, Haocheng Li, Wei Zou, Francois Mercier, Pascal Chanu, Benjamin Wu, Anthony Lee, Chunze Li, Jin Y. Jin. Tumor dynamic model-based decision support for phase Ib immunotherapy combination studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5176.